Prevent customer-defined high risk outputs
Implement safeguards or technical controls to prevent additional high risk outputs as defined in risk taxonomy
Control activities
Typical evidence
Screenshot of filtering rules, system configuration, or code showing detection logic mapped to AI risk taxonomy categories and corresponding response actions per severity level - may include risk classifiers with block/flag/log rules, content moderation API configuration defining actions by risk type, or defensive prompting.
Category
Technical ImplementationDocumentation or workflow configuration showing human review and escalation procedures for flagged content - may include runbook defining escalation criteria and review SLAs, workflow diagram showing approval process, or ticketing system configuration (Jira, Linear) with content review queues and assignment rules.
Category
Technical ImplementationScreenshot of code or system configuration showing automated response mechanisms - may include logic blocking or modifying outputs based on risk scores, or dynamic warning messages triggered by content flags.
Category
Technical ImplementationOrganizations can submit alternative evidence demonstrating how they meet the requirement.
AIUC-1 is built with industry leaders
"We need a SOC 2 for AI agents— a familiar, actionable standard for security and trust."
"Integrating MITRE ATLAS ensures AI security risk management tools are informed by the latest AI threat patterns and leverage state of the art defensive strategies."
"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."
"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."
"AIUC-1 standardizes how AI is adopted. That's powerful."
"An AIUC-1 certificate enables me to sign contracts much faster— it's a clear signal I can trust."
The Security, Safety, and Reliability standard for AI agents
Stay up to date with AIUC-1
© 2026.AIUC